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In this paper, the authors present a holistic power and performance management framework that reduces power consumption of the GPU based cluster and maintains the system performance within an acceptable predefined threshold. The framework dynamically scales the GPU cluster to adapt to the variation of incoming workload\u2019s requirements and increase the idleness of the of GPU devices, allowing them to transition to low-power state. The proposed power and performance management framework in GPU cluster demonstrated 46.3% power savings for GPU workload while maintaining the cluster performance. The overhead of the proposed framework is insignificant on the normal application\\system operations and services.<\/p>","DOI":"10.4018\/ijcac.2012100102","type":"journal-article","created":{"date-parts":[[2013,2,15]],"date-time":"2013-02-15T18:02:31Z","timestamp":1360951351000},"page":"16-31","source":"Crossref","is-referenced-by-count":11,"title":["Power and Performance Management of GPUs Based Cluster"],"prefix":"10.4018","volume":"2","author":[{"given":"Yaser","family":"Jararweh","sequence":"first","affiliation":[{"name":"Department of Computer Science, Jordan University of Science and Technology, Irbid, Jordan"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Salim","family":"Hariri","sequence":"additional","affiliation":[{"name":"Department of Electrical and Computer Engineering, University of Arizona, Tucson, AZ, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2432","reference":[{"key":"ijcac.2012100102-0","first-page":"657","article-title":"Nearest neighbor clustering: A baseline method for consistent clustering with arbitrary objective functions.","volume":"10","author":"S.Bubeck","year":"2009","journal-title":"Journal of Machine Learning Research"},{"key":"ijcac.2012100102-1","unstructured":"Distributed Computing Group of UK. 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